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2 results for “PKU GIMMS NDVI”
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2)
<p><strong>Brief Introduction:</strong></p> <p>The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.2) provides spatiotemporally consistent global NDVI data in half-month and 1/12° from 1982 to 2022. It is created to address the major uncertainties presented in current global long-term NDVI products, i.e., the effects of NOAA satellite orbital drift and AVHRR sensor degradation.</p> <p> </p> <p>The PKU GIMMS NDVI was generated based on biome-specific BPNN models that employed GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. It was then consolidated with the MODIS NDVI (MOD13C1) to extend the temporal coverage to 2022 via a pixel-wise Random Forests fusion method.</p> <p> </p> <p>The PKU GIMMS NDVI exhibits overall high accuracy evaluated by Landsat NDVI samples. Besides, it efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency with MODIS NDVI in terms of pixel value and global vegetation trend. It could potentially provide a more solid data basis for global change studies.</p> <p> </p> <p>Here we provide two versions of PKU GIMMS NDVI for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the MODIS NDVI (1982−2022). <strong>We strongly recommend an adequate use of the quality control (QC) layer in the product. </strong>Please refer to the Readme file for more details. <strong>We also recommend removing sparse vegetation by a threshold (e.g., 0.1) in trend analysis (Zhou et al., 2001; Liu et al., 2016)</strong></p> <p> </p> <p><strong>Major updates:</strong></p> <p>Version 1.0 (December 15, 2022):</p> <p>· The original version of the product.</p> <p> </p> <p>Version 1.1 (June 17, 2023):</p> <p>· A pixel-wise Random Forests consolidation method is used to replace the linear one.</p> <p>· The data files have been re-organized on a decade basis.</p> <p> </p> <p>Version 1.2 (August 17, 2023):</p> <p>· The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate NDVI values of EBF during the periods of 1982−1984 and all October to April, when the Landsat NDVI samples were relatively scarce.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN</p> <p>Projection: Geographic</p> <p>Spatial Resolution: 1/12 degree</p> <p>Temporal Resolution: Half month</p> <p>Temporal Coverage: January 1982 to December 2022</p> <p>Image Dimension: Rows-2160; Columns-4320</p> <p>Units: unitless</p> <p>Fill Value: 65535</p> <p>Data Type: uint16</p> <p>Valid Range: 0-1000</p> <p>Scale Factor: 0.001</p> <p>File Format: TIFF(.tif)</p> <p>File Size: ~8Mb each file</p> <p> </p> <p><strong>References:</strong></p> <p>Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, <a href="https://doi.org/10.5194/essd-15-4181-2023">https://doi.org/10.5194/essd-15-4181-2023</a>, 2023.</p> <p>Liu, Q., Fu, Y. H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I. A., and Piao, S.: Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology, Global Change Biology, 22, 3702–3711, <a href="https://doi.org/10.1111/gcb.13311">https://doi.org/10.1111/gcb.13311</a>, 2016.</p> <p>Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., and Myneni, R. B.: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106, 20069–20083, <a href="https://doi.org/10.1029/2000JD000115">https://doi.org/10.1029/2000JD000115</a>, 2001.</p> <p> </p>
PKU GIMMS Processed NDVI data
<p>基于最大值合成法的PKU GIMMS NDVI年、月度数据处理</p>
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